Mentoring and Faculty-Student Interaction in an Online Doctoral Program

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1 International Education Research Volume 1, Issue 4 (2013), ISSN E-ISSN Published by Science and Education Centre of North America Mentoring and Faculty-Student Interaction in an Online Doctoral Program Tera D. Simmons 1 & Linda D. Grooms 2* 1 Butler County Board of Education, Greenville, AL 2 Regent University, Virginia Beach, VA Tera D. Simmons, Ed.D., Butler County Board of Education, 211 School Highland Drive, Greenville, AL USA, Tel , *Correspondence: Linda D. Grooms, Ph.D., School of Education, Regent University, 1000 Regent University Drive, Virginia Beach, VA USA. Tel ; Abstract: This study analyzed one overarching research question: What effect does faculty-student interaction have on the relationship between student mentoring and student success in an online doctoral program. Both Cohen s (1993) Principles of Adult Mentoring Scale and Grooms and Bocarnea s (2003) Computer-Mediated Interaction Scale were modified and consolidated into one instrument to measure learners perceptions of mentoring and faculty-student interaction. Student success was operationalized as grade point average and passing comprehensive examinations on the first attempt, two critical benchmarks of doctoral program completion. While the data from this study did not support that faculty-student interaction moderated the relationship between student mentoring and student success, it did generate a plethora of questions. It also suggested the possibility that the online educational environment transmutes the traditional mentoring functions proposing the probable need for a revised instrument that will adequately measure these variables within the 21 st century technologically-rich context. Keywords: online education, adult learners, doctoral programs, student success, mentoring, interaction, faculty-student interaction 1. Introduction Across the globe, doctoral programs abound within the walls of academe and when coupled with the ubiquitous nature of 21 st century technology, the attraction and feasibility of those programs are ever-expanding. With the growing availability of such opportunities, statistics continue to reveal that even under highly favorable conditions, no more than three-quarters of adult learners who enter doctoral programs complete their degrees (Council of Graduate Schools, 2008, 2). Cognizant of this sobering statistic, two preliminary questions arise: 1) Other than degree completion, are there additional benchmarks of success at this educational level and 2) What are some of the attributing factors to facilitate that success? Science and Education Centre of North America 1

2 Tera D. Simmons & Linda D. Grooms Submitted on October 14, 2013 In his report of the Ph.D. Completion Project, a 7-year grant-funded project heavily supported by both Pfizer and the Ford Foundation, Scott Jaschik (2007), editor and cofounder of Inside Higher Ed, noted that With regard to the "main factors" contributing to completion, new Ph.D.'s (who could pick more than one item that applied) ranked the following: 80 percent cited financial support, 63 percent mentoring/advising, 60 percent family support, 39 percent social environment and peer support, 39 percent program quality, and 30 percent professional and career guidance. ( 4) Focusing on mentoring support, which includes advising as well as professional and career guidance, Johnson and Huwe (2003) espoused the benefit of having a professor to serve as a mentor to advise, guide, and support learners as they tackle the rigorous demands of graduate education. Although mentoring necessitates a relationship between two individuals (i.e., a mentor and a mentee or protégé), without some type of faculty-student interaction, this relationship would never emerge regardless of the environmental context. 1.1 The Online Environment For more than a decade, online courses, in which the learner and the professor are independent of place and time, have proliferated the landscape of higher education (Grooms, 2009; Hart, 2012; Leners, Wilson, & Sitzman, 2007). In fact, today in the digital age of the 21 st century, technological means have resulted in the Internet becoming distance education s most preponderate mode of course delivery (Dolezalek, 2003; Grooms, 2009) and as a result of online classes, universities now have the opportunity to provide the masses with unparalleled access to knowledge. Galvanizing reflection, provoking active learning, and promoting professional learning communities are but just a few of the advantages of online education (Eastmond, 1998). However, accompanying these benefits, one must also keep in mind that in order to create an atmosphere conducive to learning, professors must acclimatize pedagogical practices for the online milieu (Barczyk, Buckenmeyer, Feldman, & Hixon, 2011). For example, in order to create an environment that promotes learning and personal development, Grooms (2000, 2003) asserted that frequent faculty-student interaction is not only essential, but its need is amplified in the online context. 1.2 Faculty-Student Interaction Defined as the reciprocal or mutual influence (Eysenck, Arnold, & Meli, 1972) or mutual interdependence (Thibaut & Kelly, 1959) of individuals, several early researchers (e.g., Gresh & Mrozowski, 2000; Grooms, 2000; Miller & Lu, 2003) consistently found that faculty-student interaction was imperative for learners to be successful in the online environment. Serving numerous purposes, this interaction not only fostered course discussion and reflection (Roblyer & Ekhaml, 2000), but it also ameliorated learners perspectives and advanced them toward attaining their goals (Wagner, 1994). In addition, Daniel and Marquis (1979) advocated that interaction with others [in the distance learning environment] can temper the otherwise authoritarian style of a course and motivate the student to persevere by providing psychological support (p. 36). 2 Science and Education Centre of North America

3 International Education Research Vol. 1, Issue 4, 2013 In her study of 105 online doctoral learners, Grooms (2000) developed an instrument to ascertain the importance of interaction, while identifying five specific types. She referred to them as Informational Feedback (providing general information regarding course content, assignments, and expectations); Corrective/Evaluative Feedback (sharing information that guides, directs, and affirms); Intellectual Discussion (challenging thoughts, ideas, and beliefs); Motivation/Support (listening, providing encouragement, appreciating thoughts and feelings); and Socializing (selfdisclosure and reciprocity of empathetic information). Later Reid-Martinez and Grooms (2004) and Grooms and Reid-Martinez (2006) unveiled that these five types of interaction are inherent in a fruitful mentoring relationship. 1.3 Mentoring A mentor is described as an individual who oversees the career and development of another person, usually a junior, through teaching, counseling, providing psychological support, protecting, and at times promoting or sponsoring (Zey, 1991, p. 7). Paramount to the learning process (Cameron, 2001; Fulford & Sakaguchi, 2002), mentoring has monumental benefits (Corbett & Paquette, 2011; Howard & Turner-Nash, 2011; Zellers, Howard, & Barcic, 2008). For example, mentoring not only facilitates learning (Garvey & Alfred, 2000), but it also influences career (Gibson, 2004) and professional development (Grogan & Crow, 2004). Emphasizing its significance, Frierson (1997) proclaimed that mentoring is not only essential but it may very well be the heart of graduate education (p. 2). Identifying six mentor behavioral functions, Norman Cohen (1993) developed a self-assessment for professionals who assume the role of mentor. Claiming that these functions are essential for developing a satisfying and productive mentoring relationship, he referred to them as Relationship Emphasis (establishing trust), Informative Emphasis (providing advice), Facilitative Focus (encouraging alternatives), Confrontive Focus (challenging the protégé), Mentor Model (motivating the protégé), and Student Vision (stimulating critical thinking). In consideration of this literature, one overarching research question arose for this study: What effect does faculty-student interaction have on the relationship between student mentoring and student success in an online doctoral program? From this research question, two sets of hypotheses emerged. The first dealt with student success as defined by GPA. H 1-1 H 1-2 Under low faculty interaction, there is a relationship between student mentoring and grade point average. Under high faculty interaction, there is a relationship between student mentoring and grade point average. The second set of hypotheses dealt with student success as defined by passing comprehensive examinations on the first attempt. H 2-1 Under low faculty interaction, there is a relationship between student mentoring and passing comprehensive examinations on the first attempt. Science and Education Centre of North America 3

4 Tera D. Simmons & Linda D. Grooms Submitted on October 14, 2013 H 2-2 Under high faculty interaction, there is a relationship between student mentoring and passing comprehensive examinations on the first attempt. 2. Method To determine the effect of faculty-student interaction on the relationship between student mentoring and student success of online doctoral learners, 156 adults pursuing their doctoral degrees in education at a Judeo-Christian university in the southeastern part of the United States were solicited. With the exception of three one-week residencies, all coursework for these participants was negotiated online through the Blackboard Distributed Learning System via the Internet. To eliminate the threat of mortality due to attrition, the sampling frame for the study did not include students who had previously withdrawn or were on a temporary leave of absence. While students enrolled in this online doctoral program had seven years to successfully complete each of its three components--two years of coursework, a three-part comprehensive examination, and the dissertation--at the time of this study, not all students had approached their 7-year limit. Taking that into consideration, the study focused on the first two benchmarks of success: grade point average and successful passage of all three portions of the comprehensive examination on the first attempt. 2.1 Participant Characteristics Of the 156 online adult learners solicited, 123 responded yielding a 79% response rate, which was deemed representative of the sampling frame. Ranging in age from 24 to 67 years (M = 45), 29 were males and 93 were females; 38 were African-American, 81 were White, 2 were Hispanic, and 2 were Asian. 2.2 Instrumentation For this study, Cohen s (1993) Principles of Adult Mentoring Scale (PAMS) and Grooms and Bocarnea s (2003) Computer-Mediated Interaction Scale (CMIS) were used. Cohen developed the PAMS as a specific self-assessment instrument exclusively designed for higher education faculty to evaluate their competency as faculty mentors of undergraduate adult learners in a college context (p. 5). The PAMS measures six essential behavioral functions (Relationship Emphasis, Informative Emphasis, Facilitative Focus, Confrontive Focus, Mentor Model, and Student Vision) that Cohen identified based upon his extensive review of the literature as the behaviors required for a successful faculty mentor-adult learner mentoring relationship (p. 100). To most adequately reflect the needs of online doctoral learners, the PAMS was slightly modified. First, rather than have the faculty self-assess with the stem I, the instrument was altered to incorporate the stem My professor allowing participants to reflect upon their relationship with a faculty member whom they considered a mentor during their doctoral program. Second, the original instrument contained 55 items; however, the modified version contained 51, eliminating statements somewhat irrelevant to online education (e.g., two items related to nonverbal 4 Science and Education Centre of North America

5 International Education Research Vol. 1, Issue 4, 2013 communication, one related to a school counselor, and another related to television-based courses). Maintaining the intent of the original instrument, a five-point frequency scale was used (1 = Never, 2 = Infrequently, 3 = Sometimes, 4 = Frequently, 5 = Always). To measure online faculty-student interaction, the CMIS was considered the most appropriate instrument available. Grooms and Bocarnea (2003) developed this instrument based upon Grooms (2000) earlier version entitled the Computer-Mediated Interaction Questionnaire (CMIQ). Based upon a thorough review of the literature, the CMIQ was initially developed to measure adult learners expectations related to the importance of as well as their preferred type of interaction (Informational Feedback, Corrective/Evaluative Feedback, Intellectual Discussion, Motivation/Support, and Socializing). The CMIQ had an alpha coefficient (Cronbach s Alpha) of.86 for the entire instrument. Based solely on Grooms five types of interaction expectations, the revised CMIS consists of two parts: faculty interaction and peer interaction. The current study focused solely on faculty interaction, thus only the first half of the instrument was used and this section of the CMIS was modified to reflect learner experience rather than learner expectation. Once again, to maintain the original intent of the instrument, a seven-point Likert scale (1 = Strongly disagree, 2 = Somewhat disagree, 3 = Slightly disagree, 4 = Neutral, 5 = Slightly agree, 6 = Somewhat agree, 7 = Strongly agree) was used. For participants ease, the PAMS and the CMIS were consolidated into one instrument. To establish face validity and maintain the integrity of the new Mentoring and Interaction Questionnaire (MIQ), draft copies of the adapted instrument were reviewed and critiqued by four doctoral students in other online doctoral programs that were representative of the sampling frame. The pilot study helped to clarify directions and determine the time needed to complete this new instrument. To measure the internal consistency of the modified items, Cronbach s Alpha was performed on each section (i.e., mentoring and interaction) of the modified survey. The reliability coefficient for the total scale of the PAMS revealed an alpha coefficient of.9732, while the reliability coefficient for the total scale of the CMIS revealed an alpha coefficient of Additionally, coefficients of internal consistency for the PAMS were.9050 (Relationship Emphasis),.9072 (Informative Emphasis),.9208 (Facilitative Focus),.8709 (Confrontive Focus),.9274 (Mentor Model), and.9619 (Student Vision). 2.3 Procedures Solicited through an that summarized the study, ensured confidentiality, and provided both the instructions and the link to the MIQ, 156 adult learners were asked to voluntarily complete the twopart MIQ within an 18-day time period. To curtail the threat of a low response rate, in addition to the initial , several follow-up s and phone calls were used. 3. Results The first set of two hypotheses dealt with student success as defined by GPA. To examine patterns Science and Education Centre of North America 5

6 Tera D. Simmons & Linda D. Grooms Submitted on October 14, 2013 of relationships between multiple independent variables (Cohen s six mentoring functions) and one dependent variable (GPA), these two hypotheses were analyzed using multiple regressions via SPSS. To analyze the second set of hypotheses dealing with student success as defined by passing comprehensive examinations on the first attempt, logistic regression, which is used for making predictions when the dependent variable (passing comps) is dichotomous and the independent variable (Cohen s six mentoring functions) is either continuous or categorical, was employed. The moderating variable was faculty-student interaction. Although Grooms (2000) delineated the construct of interaction into five types (Informational Feedback, Corrective/Evaluative Feedback, Intellectual Discussion, Motivation/Support, and Socializing), for the purpose of this study, it was decided to examine the overall construct of interaction, which was dichotomized into those experiencing high faculty-student interaction and those experiencing low faculty-student interaction. Those who experienced high interaction scored above the mean, and learners experiencing low interaction scored either equal to or less than the mean. 3.1 Low Faculty-Student Interaction and Grade Point Average The first benchmark of success in this doctoral program was grade point average. GPA for the 59 respondents who experienced low faculty-student interaction (M 4.91) ranged from 2.55 to 4.0. Based on a 4.0 scale, the mean GPA was Of the six mentor behavioral functions, learners experiencing low faculty-student interaction perceived their professors exhibited more of the Mentor Model (M = 2.76), followed by the Confrontive Focus (M = 2.74), and less of the Informative Emphasis (M = 2.29; see Table 1). In other words, those experiencing low facultystudent interaction perceived faculty as more motivating and challenging to them than actually providing advice. Table 1. Descriptive statistics for GPA and mentor behavioral functions under LFI Variable Mean Standard Deviation GPA Relationship Emphasis Informative Emphasis Facilitative Focus Confrontive Focus Student Vision Mentor Model Note. N = 59. LFI = Low Faculty-Student Interaction. Under low faculty-student interaction, Pearson r indicated a slight positive relationship between GPA and the six mentor behavioral functions: Relationship Emphasis r(57) =.12, p =.19, Informative Emphasis r(57) =.15, p =.13, Facilitative Focus r(57) =.16, p =.12, Confrontive Focus r(57) =.17, p =.10, Mentor Model r(57) =.15, p =.13, and Student Vision r(57) =.13, p =.16 (see Table 2). 6 Science and Education Centre of North America

7 International Education Research Vol. 1, Issue 4, 2013 Table 2. Correlations between GPA and mentor behavioral functions under LFI RE IE FF CF MM SV GPA RE.83*.83*.76*.83*.85* IE.86*.82*.79*.91* FF.85*.86*.92* CF.80*.89* MM.90* Note. * The correlation is statistically significant at the p <.05 level. Using multiple regression, the data did not support that low faculty-student interaction moderated the relationship between student mentoring and grade point average, (R 2 =.04, adjusted R 2 = -.07, SS =.19, MS =.03, F (6, 52) =.38, p =.89). With a 95% constant confidence interval ranging from 3.20 to 3.80 (see Table 3), the overall GPA was not significantly related to the mentor behavioral functions included in this study, thus H 1-1 was rejected. Table 3. Coefficients: Multiple regression of LFI 95% Confidence Interval for B Correlations Lower Bounds Upper Bounds Zero-order Partial Part Constant RE IE FF CF MM SV High Faculty-Student Interaction and Grade Point Average GPA for the 64 respondents who experienced high faculty-student interaction (M > 4.91) ranged from 3.03 to 4.0. Based on the 4.0 scale, the mean GPA was 3.70; however, it should be noted that the GPA of high faculty-student interaction and low faculty-student interaction differed by only.02. Of the six mentor behavioral functions, learners experiencing high faculty-student interaction perceived their professors exhibited more Student Vision (M = 3.95) followed by Facilitative Focus (M = 3.93). Analogous to the results under low faculty-student interaction, students experiencing high faculty-student interaction perceived their professors exhibited less of the Informative Emphasis (M = 3.34; see Table 4). In other words, those experiencing high faculty-student interaction perceived that faculty stimulated their critical thinking and encouraged alternatives more Science and Education Centre of North America 7

8 Tera D. Simmons & Linda D. Grooms Submitted on October 14, 2013 so than providing advice. In comparison, the mean of the six mentor behavioral functions was greater for students who experienced high faculty-student interaction than for the students experiencing low faculty-student interaction. Table 4. Descriptive statistics for GPA and mentor behavioral functions under HFI Variable Mean Standard Deviation GPA Relationship Emphasis Informative Emphasis Facilitative Focus Confrontive Focus Student Vision Mentor Model Note. N = 64. HFI = High Faculty-Student Interaction Pearson r indicated that under high faculty-student interaction, the mentor behavioral functions and GPA had a small positive relationship: Relationship Emphasis r(62) = -.01, p =.47, Informative Emphasis r(62) = -.11, p =.19, Facilitative Focus r(62) = -.03, p =.40, Confrontive Focus r(62) = -.08, p =.26, Mentor Model r(62) = -.08, p =.27, and Student Vision r(62) = -.08, p =.28 (see Table 5). Table 5. Correlations between GPA and mentor behavioral functions under HFI RE IE FF CF MM SV GPA RE.77*.74*.74*.79*.80* IE.78*.71*.79*.82* FF.66*.81*.81* CF.77*.82* MM.88* Note. * The correlation is statistically significant at the p <.05 level Once again using multiple regression, student mentoring of the learners experiencing high facultystudent interaction predicted less than 4% of the variance of GPA. The moderating variable, high faculty-student interaction, did not have a significant effect on the relationship between student mentoring and GPA (R 2 =.04, adjusted R 2 = -.07, SS =.10, MS =.02, F (6, 57) =.36, p =.90). The 95% confidence interval for each mentor behavioral function under high faculty-student interaction contained the value of zero, and therefore overall GPA was not significantly related to these functions for these students either (see Table 6). According to this study H 1-2 was also rejected. 8 Science and Education Centre of North America

9 International Education Research Vol. 1, Issue 4, 2013 Table 6. Coefficients: Multiple regression of HFI 95% Confidence Interval for B Correlations Lower Bounds Upper Bounds Zero-order Partial Part Constant RE IE FF CF MM SV Low Faculty-Student Interaction and Passing Comprehensive Examinations Once the learners successfully complete all required courses for the doctoral program, they are administered a three-part comprehensive examination, which was the second benchmark of success. At the time of this study, 86 of the 123 participants had taken this exam. Of those experiencing low faculty-student interaction, 27 respondents passed all three sections on the first attempt; however, 12 did not. According to the Cox and Snell R 2 (.18) and Nagelkerke R 2 (.26), the mentor behavioral functions accounted for 18% to 26% of students passing the exam. The data revealed a negative relationship between passing comprehensive examinations on the first attempt and Relationship Emphasis, Informative Emphasis, and Mentor Model; however, positive relationships were distinguished between passing comprehensive examinations and Facilitative Focus, Confrontive Focus, and Student Vision (see Table 7). In other words, students who passed comprehensive examinations on the first attempt rated their professors higher for encouraging alternatives, challenging them, and stimulating their critical thinking than those who did not initially pass, although it was still considered insignificant. Table 7. Logistic regression: LFI Variables B SE Wald p Exp (B) RE IE FF CF MM SV Constant Science and Education Centre of North America 9

10 Tera D. Simmons & Linda D. Grooms Submitted on October 14, 2013 The 95% constant confidence interval ranged from.14 to (see Table 8). A chi-square goodness-of-fit test was used to evaluate the change in 2 log likelihood to determine if mentor behavioral functions as a whole improved the model fit when predicting passing the comprehensive examination. In this study, the 2 log likelihood was insignificant (χ 2 (6) = 7.87, p =.25), thus H 2-1 was rejected. Table 8. 95% Confidence interval for LFI Variable Lower Upper RE IE FF CF MM Constant High Faculty-Student Interaction and Passing Comprehensive Examinations Of the learners experiencing high faculty-student interaction, 32 respondents successfully passed comprehensive examinations on the first attempt and 15 did not. According to Cox and Snell R 2 (.03) and Nagelkerke R 2 (.04), student mentoring accounted for 3% to 4% of passing the exam. As shown in Table 9, the analysis indicated a negative relationship between passing the comprehensive examination on the first attempt and Mentor Model and Student Vision, while positive relationships existed between the other mentor behavioral functions (i.e., Relationship Emphasis, Informative Emphasis, Facilitative Focus, and Confrontive Focus.). Table 9. Logistic regression: HFI Variables B SE Wald p Exp (B) RE IE FF CF MM SV Constant The range of the 95% confidence interval for high faculty-interaction was not as extensive as compared to low interaction (see Table 10). According to the results yielded from logistic regression, the data do not support a relationship between student mentoring and passing comprehensive examinations on the first attempt (χ 2 (6) = 1.19, p =.98). The chi-square goodness-of-fit test for the 10 Science and Education Centre of North America

11 International Education Research Vol. 1, Issue 4, 2013 regression models at both high and low interaction did not differ, thus faculty-student interaction did not moderate the relationship between student mentoring and student success, therefore H 2-2 was also rejected. Table % Confidence interval for HFI Variable Lower Upper RE IE FF CF MM Constant Discussion The data from this study did not support that faculty-student interaction moderated the relationship between student mentoring and student success. Neither low (M 4.19) nor high (M 4.91) faculty-student interaction had an effect on the relationship between student mentoring and student success as measured by grade point average and passing comprehensive exams on the first attempt. In analyzing grade point averages, the mean of the mentor behavioral functions was greater for students experiencing high faculty-student interaction than for learners experiencing low facultystudent interaction; therefore, learners experiencing high faculty-student interaction scored their professors ability to facilitate the mentor behavioral functions higher than those doctoral learners who experienced low faculty-student interaction. Under low faculty-student interaction, there was a negative correlation between passing comprehensive examinations on the first attempt and three of the six mentor behavioral functions: (Relationship Emphasis (establishing trust), Facilitative Focus (encouraging alternatives), and Mentor Model (motivating the protégé). Under high faculty-student interaction, there was a negative correlation between passing comprehensive examinations and two mentor behavioral functions: Mentor Model (motivating the protégé) and Student Vision (stimulating critical thinking), thus learners expressed that their professors portrayed these mentor behavioral functions less frequently. One would surmise that since interaction is a significant part of the mentoring relationship, it would be pivotal to student success, thus the results of this study were quite surprising opening a Pandora s Box of inquiries that should be explored in future studies. For the ease of the reader, these suggestions for future research have been clustered. First, if mentoring components were embedded in the curriculum, would it make a significant impact on adult learners perceptions as well as academic outcomes, as Reid-Martinez and Hunt (1998) found in their preliminary research on the implementation of this in an existing doctoral Science and Education Centre of North America 11

12 Tera D. Simmons & Linda D. Grooms Submitted on October 14, 2013 program? With the elements embedded in the curriculum, the professors would not necessarily need to make a conscientious effort to function in the complete mentor role, which is comprised of all six mentor behavioral functions, yet these crucial components would be prevalent in the course design, which would then facilitate each learner receiving the same quantity and quality of mentoring. Second, although this study focused on mentoring in an online doctoral program, it might be interesting to compare online programs with the traditional face-to-face environment. Would adult learners rate the needs for mentoring and faculty-student interaction differently dependent on the mode of program delivery? Third, while GPA is commonly used in the literature to measure student success and thus it was chosen as one of the benchmarks for this study, in most doctoral programs learners must maintain a 3.0 to remain in the program, thus discrepancies in this measure are often minimal. This causes one to conjecture if other possible variables such as degree completion rate might more appropriately measure student success. Fourth, while research revealed that mentoring has an impact on students completing graduate studies, does it specifically have an effect on doctoral learners completing their dissertations? Would intensive mentoring and faculty-student interaction increase the number of doctoral graduates as well as decrease the amount of time required to complete the dissertation phase of the online program? Would faculty-student interaction moderate the relationship between student mentoring and completing the dissertation phase of the program? Analysis of not only graduates but those who have dropped out might provide information to distinguish the factors that attribute to completing the program. Are the students who remain in the program a self-selecting group that desires to get the job done thus requiring less frequent mentoring and interaction than those who cease to persevere? A longitudinal study focusing on the factors affecting completion rate might be insightful. Last, the development of a different instrument to more accurately measure the functions of online mentoring should be considered. Whereas Cohen s (1993) PAMS measures six mentor behavioral functions in the face-to-face environment, an instrument that incorporates Jacobi s (1991) original 15 mentoring functions might further delineate this construct. More so, one must question if it is possible that mentoring functions in an online educational environment differ completely from the ones Jacobi and Cohen identified. 5. Conclusion Focused on a specific sampling frame of 156 online doctoral learners, these findings should be interpreted circumspectly and generalizations should only be made to groups with analogous characteristics. Replications in other online programs could help reinforce its insinuations. And while this study generated more questions than it answered, it did provide a pivotal piece of research in both the fields of faculty-student interaction and mentoring as they relate to success in an online doctoral program. This reminds us that the field is rich with opportunities to further 12 Science and Education Centre of North America

13 International Education Research Vol. 1, Issue 4, 2013 explore these concepts in light of our rapidly advancing technology of the 21 st century. References [1] Barczyk, C., Buckenmeyer, J., Feldman, L., & Hixon, E. (2011). Assessment of a universitybased online education mentoring program from a quality management perspective. Mentoring & Tutoring, 19(1), [2] Cameron, W. R. (2001). Mentoring and locus of control. Dissertation Abstracts International, 62(05), 2532B. [3] Cohen, N. H. (1993). Development and validation of the principles of adult mentoring scale for faculty mentors in higher education. Dissertation Abstracts International, 52(02), 400A. [4] Corbett, F., & Paquette, K. (2011). An investigation of mentorship as perceived by university faculty, teaching associates, and graduate assistants. Education, 132(2), [5] Council of Graduate Schools (2008). The Ph.D. completion project. Retrieved from [6] Daniel, J. S., & Marquis, C. (1979). Interaction and interdependence: Getting the mixture right. Teaching at a Distance, 15, [7] Dolezalek, H. (2003). Collaborating in cyberspace. Training, 40(4), 32-34, [8] Eastmond, D. V. (1998). Adult learners and Internet-based distance education. In B. Cahoon (Ed.), Adult learning and the Internet (pp ). San Francisco: Jossey-Bass. [9] Eysenck, H. J., Arnold, W., & Meili, R. (Eds.). (1972). Encyclopedia of psychology. NY: The Continuum Publishing Company. [10] Frierson, H. T. (1997). Introduction. In H. T. Frierson (Ed.), Diversity in higher education (pp. 1-5). Greenwich, CT: JAI Press. [11] Fulford, C. P., & Sakaguchi, G. (2002). Validating a taxonomy of interaction strategies for two-way interactive distance education television. International Journal of Instructional Media, 29(1), [12] Garvey, B., & Alfred, G. (2000). Developing mentors. Career Development International, 5(4/5), [13] Gibson, S. K. (2004). Being mentoring: The experience of women faculty. The Journal of Career Development, 30(3), [14] Gresh, K. S., & Mrozowski, S. (2000). Faculty/student interaction at a distance: Seeking balance. (IR ). Paper presented at Nashville, TN: Thinking IT Through Proceedings and Post-Conference Materials (ERIC Document Reproduction Service No. ED452805) [15] Grogan, M., & Crow, G. (2004). Mentoring in the context of educational leadership preparation and development: Old wine in new bottles. Educational Administration Quarterly, Science and Education Centre of North America 13

14 Tera D. Simmons & Linda D. Grooms Submitted on October 14, (4), [16] Grooms, L. D. (2000). Interaction in the computer-mediated adult distance learning environment: Leadership development through online education. Dissertation Abstracts International, 61(12), 4692A. [17] Grooms, L. D. (2003). Computer-mediated communication: A vehicle for learning. International Review of Research in Open and Distance Learning, 4(2). Retrieved from [18] Grooms, L. D. (2009). Distance learning overview. In Encyclopedia of Information Science and Technology (2 nd ed.). (Vol. 3, pp ). Hershey, PA: IGI Global. [19] Grooms, L. D., & Bocarnea, M. C. (2003). Computer mediated interaction scale. Unpublished manuscript, Regent University. [20] Grooms, L. D., & Reid-Martinez, K. (2006, November). Crossroads: The intersection of mentoring, culture, and learning environment in contemporary leadership education. Presentation at the 8 th annual International Leadership Association Conference, Chicago, IL. [21] Hart, C. (2012). Factors associated with student persistence in an online program of study: A review of the literature. Journal of Interactive Online Learning, 11(1), [22] Howard, K. J., & Turner-Nash, K. (2011). Alimentar: Theorizing pedagogy, curriculum, and mentorship for democratic doctoral education. Equity & Excellence in Education, 44(1), [23] Jaschik, S. (2007). Why and when Ph.D. students finish. Inside Higher Ed. Retrieved from [24] Johnson, W. B., & Huwe, J. M. (2003). Getting mentored in graduate school. Washington, DC: American Psychological Association. [25] Leners, D. W., Wilson, V. W., & Sitzman, K. L. (2007). Twenty-first century doctoral education: Online with a focus on nursing education. Nursing Education Perspectives, 28(6), [26] Miller, M., & Lu, M.-Y. (2003). Serving the non-traditional students in e-learning environments: Building successful communities in the virtual campus. Education Media International, 40(1-2), [27] Reid-Martinez, K. & Grooms, L. D. (2004, November). Mentoring functions in a computermediated learning environment & mentoring considerations of communication and interaction in the virtual environment. Presentation at the 90 th annual convention of the National Communication Association, Chicago, IL. [28] Reid-Martinez, K., & Hunt, C. (1998). Role and consequences of online adult academic mentoring: A case study. Paper presented at the International Mentoring Association Annual Conference, Phoenix, AR. 14 Science and Education Centre of North America

15 International Education Research Vol. 1, Issue 4, 2013 [29] Roblyer, M. D., & Ekhaml, L. (2000). How interactive are your distance courses? A rubric for assessing interaction in distance learning. Retrieved from /%7Edistance/roblyer32.html [30] Thibaut, J. W., & Kelly, H. H. (1959). The social psychology of groups. NY: John Wiley & Sons, Inc. [31] Wagner, E. D. (1994). In support of a functional definition of interaction. American Journal of Distance Education, 8(2), [32] Zey, M. G. (1991). The mentor connection. New Brunswick, NJ: Transaction Publishers. [33] Zellers, D., Howard, V., & Barcic, M. (2008) Faculty mentoring programs: Reenvisioning rather than reinventing the wheel. Review of Educational Research, 78(3), doi: / Science and Education Centre of North America 15

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